Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014. Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A. J., Freire, S., Halkia, M., Julea, A. M., Kemper, T., Soille, P., & Syrris, V. Volume 27741 EN , Publications Office of the European Union. ![Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014 [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.
@book{pesaresiOperatingProcedureProduction2016,
title = {Operating Procedure for the Production of the Global Human Settlement Layer from {{Landsat}} Data of the Epochs 1975, 1990, 2000, and 2014},
author = {Pesaresi, Martino and Ehrlich, Daniele and Ferri, Stefano and Florczyk, Aneta J. and Freire, Sergio and Halkia, Matina and Julea, Andreea M. and Kemper, Thomas and Soille, Pierre and Syrris, Vasileios},
date = {2016},
volume = {27741 EN},
publisher = {{Publications Office of the European Union}},
location = {{Luxembourg}},
issn = {1831-9424},
doi = {10.2788/253582},
url = {http://mfkp.org/INRMM/article/14601634},
abstract = {A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.},
isbn = {978-92-79-55012-6},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14601634,~to-add-doi-URL,computational-science,data-transformation-modelling,ghsl,global-scale,landsat,modelling,remote-sensing,urban-areas},
pagetotal = {67}
}
Downloads: 0
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